GPU Computing Gems Emerald Edition (Applications of GPU Computing Series)

Posted by Marwan Abd Ellah on Saturday, March 31, 2012 Under: GPGPU

"...the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010

Graphics processing units (GPUs) can do much more than render graphics. Scientists and researchers increasingly look to GPUs to improve the efficiency and performance of computationally-intensive experiments across a range of disciplines.

GPU Computing Gems: Emerald Edition brings their techniques to you, showcasing GPU-based solutions including:

  • Black hole simulations with CUDA
  • GPU-accelerated computation and interactive display of molecular orbitals
  • Temporal data mining for neuroscience
  • GPU -based parallelization for fast circuit optimization
  • Fast graph cuts for computer vision
  • Real-time stereo on GPGPU using progressive multi-resolution adaptive windows
  • GPU image demosaicing
  • Tomographic image reconstruction from unordered lines with CUDA
  • Medical image processing using GPU -accelerated ITK image filters
  • 41 more chapters of innovative GPU computing ideas, written to be accessible to researchers from any domain

GPU Computing Gems: Emerald Edition is the first volume in Morgan Kaufmann's Applications of GPU Computing Series, offering the latest insights and research in computer vision, electronic design automation, emerging data-intensive applications, life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, and video / image processing.


  • Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more

  • Many examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution

  • Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use

In : GPGPU 


Tags: gpu gpgpu "parallel computing" 
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